Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

Climatic Change (2019) 156:139–150
https://doi.org/10.1007/s10584-019-02544-0

ESSAY

Linking scales and disciplines: an interdisciplinary
cross-scale approach to supporting climate-relevant
ecosystem management

Christian Berger 1 & Mari Bieri 2 & Karen Bradshaw 3 & Christian Brümmer 2 &
Thomas Clemen 4 & Thomas Hickler 5,6 & Werner Leo Kutsch 7 & Ulfia A. Lenfers 4 &
Carola Martens 5,6 & Guy F. Midgley 8 & Kanisios Mukwashi 2 & Victor Odipo 1 &
Simon Scheiter 5 & Christiane Schmullius 1 & Jussi Baade 9 & Justin C. O. du Toit 10 &
Robert J. Scholes 11 & Izak P. J. Smit 12,13 & Nicola Stevens 8 & Wayne Twine 14

Received: 30 August 2018 / Accepted: 27 August 2019 / Published online: 5 September 2019
# The Author(s) 2019

Abstract
Southern Africa is particularly sensitive to climate change, due to both ecological and socio-
economic factors, with rural land users among the most vulnerable groups. The provision of
information to support climate-relevant decision-making requires an understanding of the
projected impacts of change and complex feedbacks within the local ecosystems, as well as
local demands on ecosystem services. In this paper, we address the limitation of current
approaches for developing management relevant socio-ecological information on the projected
impacts of climate change and human activities. We emphasise the need for linking disciplines
and approaches by expounding the methodology followed in our two consecutive projects.
These projects combine disciplines and levels of measurements from the leaf level
(ecophysiology) to the local landscape level (flux measurements) and from the local household
level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of
models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological
approaches, as proposed here, are needed to compliment reductionist and linear, scale-
specific approaches. Decision support systems are used to integrate and communicate the data
and models to the local decision-makers.

1 Introduction

Observed temperature increases over large parts of South Africa during the period 1931–2015
have occurred at rates of about twice the global mean, and this trend is projected to continue
into the future (DEA 2017). Other projections across Southern Africa include changes in
rainfall amount, variability, intensity and seasonality, and increases in the likelihood of extreme

* Mari Bieri
  mari.bieri@thuenen.de

Extended author information available on the last page of the article

140                                                               Climatic Change (2019) 156:139–150

weather events (DEA 2017; Tadross et al. 2017). These changes are likely to alter the carbon
sink/source strength of the ecosystems exposed to them. The effects of climate changes on
individual organisms translate to changes in populations and, through altered interactions with
other species, to changes in communities and ecosystems. Indirect effects can also occur via
climate change impacts on disturbance regimes, such as fire frequency, intensity or season,
flood exposure, wind storms, and droughts (Davis-Reddy and Vincent 2017).
    The complexity of climate-management interactions in Southern Africa is well illustrated
by increasing woody biomass in savannas and former treeless grasslands (e.g. Stevens et al.
2016; Skowno et al. 2017). This phenomenon, called bush encroachment, greatly affects the
portfolio of ecosystem services provided by the system. It has traditionally been attributed to
inappropriate land management, for instance the suppression of fires due to excessive grazing
pressure. However, the impacts of increasing atmospheric CO2 might be particularly strong in
savanna ecosystems, characterised by an uneasy co-dominance by trees and grasses. In
experimental situations, increased atmospheric concentrations of CO2 create a competitive
benefit for C3 woody vegetation in relation to C4 grasses (Bond and Midgley 2012; Midgley
and Bond 2015). Stevens et al. (2016) showed a strong trend of woody plant encroachment
across various savanna and former grassland land-use types in South Africa over the past
70 years, indicating that the trend is unlikely to be attributable solely to poor management.
However, the frequent observation of bush encroachment on a managed site, but not on an
adjacent one, suggests that management factors also contribute to the phenomenon. Similarly
to the indirect effects described above for climate change, CO2 enrichment that leads to woody
encroachment will influence grass productivity with cascading effects on agricultural produc-
tion (Anadon et al. 2014), fire frequency and intensity (Langevelde et al. 2003), and biodi-
versity (Smit and Prins 2015; Stanton et al. 2018).
    Southern African terrestrial ecosystems are strongly affected by human activities, for
instance through grazing and browsing by domestic livestock, cultivation, and fuelwood
collection (Niang et al. 2014; Stevens et al. 2015). Fuelwood collection has resulted in
substantial changes in savanna composition and structure in many parts of South Africa where
large rural populations either occur in the absence of modern energy services or are unable to
afford them (Higgins et al. 1999; Giannecchini et al. 2007; Fisher et al. 2012; Kahn et al. 2012;
Matsika et al. 2013). Humans also influence the fire regime, which largely controls tree
dominance in savanna ecosystems (Scheiter and Higgins 2009; Midgley and Bond 2015;
Archibald 2016). Land cover transformation, such as the expansion of settlements and
rangelands, is very evident in certain regions (Coetzer et al. 2010; Schoeman et al. 2013).

2 Knowledge gaps and research needs

The abovementioned multiple, interacting, and contrasting impacts of climate and human man-
agement make projections of vegetation change in Southern African ecosystems difficult,
emphasising the need for long-term monitoring in conjunction with carefully designed experi-
mentation (Bond and Midgley 2012; Midgley and Bond 2015; López-Ballesteros et al. 2018). A
comprehensive analysis of possible Southern African vegetation shifts due to climate change does
not yet exist. Moreover, our understanding of the carbon dynamics of African ecosystems is
incomplete due to a paucity of long-term observations in major ecosystem types (Valentini et al.
2014). Even though savanna ecosystems cover more than half of the Southern African land area
(Cowling et al. 2004), their carbon budget and its projected changes are uncertain. A further third

Climatic Change (2019) 156:139–150                                                             141

of the area is occupied by semi-arid dwarf-shrublands (the Nama-Karoo and Succulent Karoo
Biomes), even less well studied with respect to climate responses and carbon dynamics. Knowl-
edge of CO2 fluxes is particularly lacking for areas under human land-use (e.g. Ciais et al. 2011),
even though four-fifths of South African savanna, grassland, and shrubland ecosystems are under
some form of agriculture, usually involving livestock (Kotze and Rose 2015).
   Land-use approaches that respond to ecosystem change are based on managing the
resilience of the local socio-ecological system (e.g. Linkov et al. 2014) and could be made
more robust by incorporating the knowledge of local land users. At the same time, land
management tools must be informed by credible disciplinary knowledge, since the future
circumstances may be outside the range of variation experienced in the past. However, outside
of formal conservation lands, communication between researchers and local land users and
land managers is often inadequate in Southern Africa (Ziervogel et al. 2014).
   Ecosystem management support needs to allow for interaction and exchange between
researchers of different disciplines, and between the formal scientific approaches and the
experiential reality of affected stakeholders. Many knowledge gaps exist in the continuum
from local- to global-scale change drivers and their consequences. These gaps compromise the
development of both optimal local adaptations and national, regional, and global mitigation
objectives. There is considerable complexity in the way the various components of these socio-
ecological systems interact. Potentially nonlinear interlinkages between climate change im-
pacts and land management make the response of the systems difficult to predict, especially
since the feedbacks between ecosystem responses to climate and land-use changes, and
atmospheric greenhouse gas concentrations, are poorly known. The resulting complexity
requires innovative thinking and best-of-breed technology, e.g. social-ecological models and
decision support information systems (Linkov et al. 2014).

3 Design of a cross-scale interdisciplinary approach

The complexity described above can only be understood through a scientific approach that
combines several disciplines, conducts observations at a range of scales from single plants to
ecosystems, and integrates land management activities. Using our two consecutive projects—
Adaptive Resilience in Southern African Ecosystems (ARS AfricaE, 2014–2018) and Eco-
system Management Support for Climate Change in Southern Africa (EMSAfrica, 2018–
2021)—as examples, we discuss the approach with a specific focus on the interlinkages
between the different disciplines and scales.
   The ARS AfricaE and EMSAfrica field site designs allow us to distinguish between land-
use and climate-induced impacts on the structure and function of ecosystems. We established
three focal areas along an aridity gradient in South Africa, with each area containing two
contrasting observation sites based on different intensities of land use (and thus disturbance
regimes), for instance, protected ecosystems compared with livestock grazing or peri-urban
landscapes (Fig. 1). On these sites, we measure plant ecophysiological traits, monitor
ecosystem-scale carbon fluxes, characterise the spatial dynamics of vegetation structure using
remote-sensing, and conduct socio-economic surveys on human use of the ecosystems. The
data are used to (i) create, calibrate, and test local ecosystem models, (ii) scale up the
information to the biome level, and (iii) provide information adapted to the needs of land-
use decision-makers by employing state-of-the-art multi-agent modelling and simulation
techniques to integrate spatiotemporal ecological data with social-ecological data (Fig. 2).

142                                                                      Climatic Change (2019) 156:139–150

Fig. 1 Overview of the ARS AfricaE/EMSAfrica project research sites. The image backdrop is based on the
Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) data (product ID GMYD09Q1) and represents a
composite of Normalized Difference Vegetation Index (NDVI) values observed between April 30 and May 7,
2018. The NDVI is a dimensionless indicator of live green vegetation and ranges between − 1 and 1. Here, light
to dark green colours represent a higher abundance of healthy vegetation, whereas light to dark brown tones
suggest lower amounts of photosynthetically active plant biomass. Land-use type and mean annual precipitation
are provided in the table on the right

3.1 Studying ecophysiological responses to climate change at the plant level

By using ecophysiological approaches, we can explore small-scale mechanisms at leaf or plant
level, underpinning changes at higher organisational scales such as the community or ecosystem
scale (Ainsworth et al. 2016). In our cross-level approach, ecophysiological field experiments are
used to improve our understanding of how higher-level responses, such as canopy flux, are
related to changes in short-term drivers such as humidity, temperature, and soil water. We collect
data for key shrubland, savanna, and grassland species using manipulation experiments and in-
field sampling. The traits examined include (1) vegetation structure; (2) water use and leaf-level
gas exchange under a range of soil, water, and temperature conditions, and following herbivory;
and (3) soil respiration at a range of soil, water, and temperature conditions.
    In our cross-scale approach, the results are used to parameterise ecosystem models (the
“Modelling ecosystem dynamics at various spatial and temporal scales” section) in order to
predict the functioning of entire ecosystems under future climatic and management conditions.
The soil respiration and photosynthesis measurements support the partitioning of net ecosys-
tem carbon exchange (NEE) efforts from the nearby eddy covariance (EC) flux tower into its
various components (the “Monitoring biosphere-atmosphere exchange of carbon dioxide and
water vapor” section): gross primary production (GPP) and net primary production (NPP), and
auto- and heterotrophic respiration. The results allow us to deduce responses under the varying
environmental conditions brought about by climate change. Similar comparative experiments
undertaken at the other project sites allow us to draw conclusions about how general or specific
the responses to environmental factors are.

3.2 Monitoring biosphere-atmosphere exchange of carbon dioxide and water vapor

Understanding ecosystem carbon fluxes and stocks is important for predicting the responses to
climate change and choosing climate change adaptation measures. We established EC flux
towers at all six of our project sites (Fig. 1), providing continuous and long-term monitoring of

Climatic Change (2019) 156:139–150                                                                           143

Fig. 2 Summary of the integrative project approach, showing the multi-level observations of carbon fluxes (the
“Monitoring biosphere-atmosphere exchange of carbon dioxide and water vapor” section) and plant ecophysiol-
ogy (the “Studying ecophysiological responses to climate change at the plant level” section), remote-sensing (the
“Leveraging Earth Observation data to support ecosystem monitoring, modelling, and management” section), and
socio-economic surveys (the “Understanding human impact in ecosystem change” section). These data are used to
create, calibrate, and test vegetation (the “Modelling ecosystem dynamics at various spatial and temporal scales”
section) and agent-based models which, in turn, are used to simulate and up-scale relevant information products
for land-use decision-makers (the “Integrating models to support climate-relevant decision-making” section)

biosphere-atmosphere exchange of CO2 and water vapor along the chosen aridity gradient as
well as under different land-use management regimes.
   Networks of EC flux towers with their associated meteorological measurements allow GPP
and evapotranspiration to be quantified in a variety of climate zones and vegetation types (e.g.
Brümmer et al. 2012). EC is the currently preferred method for continuously measuring
exchanges of CO2, water vapor and sensible heat between ecosystems, and the atmosphere
over time scales of hours to decades and at the landscape scale, thus enabling the evaluation of
seasonal and interannual variability as well as the elucidation of their climatic controls
(Baldocchi et al. 2001). As the productivity of water-limited ecosystems such as shrublands
and savannas is highly dependent on rainfall, and interannual differences are typically signif-
icant (e.g. Veenendaal et al. 2004; Brümmer et al. 2008, 2009; Merbold et al. 2009), long-term
measurements are essential to detect significant trends.
   In our cross-scale approach, by linking flux data with on-site ecophysiological measure-
ments and employing combined approaches from Earth Observation (EO) (the “Leveraging
Earth Observation data to support ecosystem monitoring, modelling, and management”
section) and vegetation modelling (the “Modelling ecosystem dynamics at various spatial
and temporal scales” section), we are able to improve our interpretation and understanding of
the carbon fluxes between the biosphere and atmosphere, and study the consequences of
ecosystem change for processes such as NPP, which is the basis of many ecosystem services.
Continuous measurements allow us to observe the impacts of short-term ecosystem perturba-
tions, such as changes in management regime or weather anomalies, on the CO2 exchange of

144                                                              Climatic Change (2019) 156:139–150

entire ecosystems. In the long term, the measurements will help to improve our understanding
of the net carbon balance of Southern African ecosystems.

3.3 Leveraging Earth Observation data to support ecosystem monitoring, modelling,
and management

EO data and products enable interdisciplinary studies at all scales of analysis, from the plant
and household to the landscape and regional level (cf. Fig. 2). A comprehensive set of
analysis-ready EO time series data, so-called space-time data cubes (cf. Baumann 2017) are
collected on each of the project field sites (cf. Fig. 1) and for larger geographical areas (e.g.
Kruger National Park). They consist of multi-temporal geospatial data from ground-based, air-
and space-borne platforms at various sensing schemes. The pre-processed time series data
comprise multispectral, thermal infrared, synthetic-aperture radar (SAR) as well as light
detection and ranging (LiDAR) imagery and products that serve our diverse project applica-
tions and research topics. They are obtained at multiple spatial resolutions, with ground
sampling distances (i.e. pixel sizes) ranging from a few centimetres to kilometres.
    We further develop and test data fusion and analysis schemes by extracting image products,
thematic maps, and spatial statistics from available EO data sets (e.g. Urbazaev et al. 2015;
Odipo et al. 2016). Further emphasis is on computational approaches taking advantage of and
adding value to publicly available satellite imagery such as data from NASA’s Landsat missions
and ESA’s Sentinel (Copernicus) programme (e.g. Cremer et al. 2018; Urban et al. 2018). The
resulting methods are used to derive land surface parameters related to the status and dynamics
of South Africa’s terrestrial ecosystems (e.g. fuel biomass, woody cover, vegetation heights,
land use) to implement environmental and socio-ecological mapping, monitoring, and man-
agement with direct societal benefits (e.g. Urbazaev et al. 2015; Odipo et al. 2016; Urban et al.
2018). An example application is the spatiotemporal characterisation of fuel biomass and fuel
moisture content for improved fire management in the Kruger National Park.
    In the cross-scale interdisciplinary approach, the EO data and products support the interpre-
tation of EC fluxes (the “Monitoring biosphere-atmosphere exchange of carbon dioxide and water
vapor” section), ecophysiological experiments (the “Studying ecophysiological responses to
climate change at the plant level” section) and socio-economic surveys (the “Understanding
human impact in ecosystem change” section). Moreover, they help to parameterise, calibrate, and
validate our agent-based simulations, vegetation models (the “Monitoring biosphere-atmosphere
exchange of carbon dioxide and water vapor” section) and biome shift predictions. Furthermore,
they form an integral part of our anticipated system for data-driven and science-informed
decision-making (the “Integrating models to support climate-relevant decision-making” section).

3.4 Understanding human impact in ecosystem change

The paired sites approach of the two projects aims at allowing comparisons between ecosystems
under little human impact and those under high-intensity human management in all measure-
ment scales. In addition, a case study on the local use of fuelwood is conducted around one of
the observation sites, Agincourt village in Bushbuckridge. The socio-economic conditions in
the Agincourt site are well researched due to the existence of the 27-year Agincourt Health and
Socio-Demographic Surveillance System (HDSS). Surveys conducted on fuelwood collection
and use and the monitoring of fuelwood removal in the areas surrounding the villages allow us
to accurately quantify carbon removal from local ecosystems. Longitudinal survey data enable

Climatic Change (2019) 156:139–150                                                           145

the study of interannual variation in the provision of ecosystem services to local communities,
as well as the key socio-economic drivers of household dependence on these. In the interdis-
ciplinary approach necessary for socio-ecological inquiry, the results are used in conjunction
with vegetation models (the “Modelling ecosystem dynamics at various spatial and temporal
scales” section) to construct a case study of resource use in local communities impacted by
climate change. To investigate whether local resource extraction and carbon removal by human
appropriation can be tracked from space, a further case study on the linkage between remotely
sensed dynamics of woody vegetation and the Agincourt household survey data is envisaged.

3.5 Modelling ecosystem dynamics at various spatial and temporal scales

Dynamic Global Vegetation Models (DGVMs) integrate processes from the leaf level to the
ecosystem and the biosphere level (Prentice et al. 2007; Smith et al. 2014). They simulate the
distribution of competing plant functional types (PFTs) and different biome types, vegetation
dynamics and structure, and the fluxes of carbon, water, and, increasingly, nutrients between
the soil, vegetation, and the atmosphere. Disturbances, such as fire (Scheiter and Higgins 2009;
Rabin et al. 2017) and grazing (e.g. Pachzelt et al. 2015) are included in some DGVMs. Site-
scale or even farm-scale applications (e.g. within the ARS AfricaE project) make it necessary
to adjust the models for local conditions (e.g. Hickler et al. 2012; Seiler et al. 2014).
   Several DGVMs have limited applicability in savanna and shrubland ecosystems, because
they do not allow for an accurate representation of the vulnerability of woody plants to fire
(Scheiter and Higgins 2009) and the vulnerability of woody plants and grasses to herbivory
(Scheiter and Higgins 2012). In addition, most DGVMs do not represent shrub growth forms
adequately (Gaillard et al. 2018). In our projects, we use the adaptive Dynamic Global Vegetation
Model (aDGVM), an individual-based model that was developed to simulate the response of
tropical vegetation to impacts of climate change, fire (Scheiter and Higgins 2009; Higgins and
Scheiter 2012), and human management, e.g. grazing and wood harvesting (Scheiter et al. 2019),
as well as LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) (Smith et al. 2001), a
global-scale model that will be adjusted for applications in southern Africa.
   In our cross-scale approach, information obtained from ecophysiological (the “Studying
ecophysiological responses to climate change at the plant level” section) and EC measurements
(the “Monitoring biosphere-atmosphere exchange of carbon dioxide and water vapor” section)
collected at the project sites is used to parameterise the models for local vegetation under
varying grazing pressures. Remote-sensing data (the “Leveraging Earth Observation data to
support ecosystem monitoring, modelling, and management” section) are used to test and
benchmark model outputs. After successful model parameterisation and testing at various
spatial scales, the aDGVM is used to estimate recent past, present, and future climate-driven
changes in vegetation and ecosystem functioning.

3.6 Integrating models to support climate-relevant decision-making

A decision support system (DSS) is a platform for integrating, analysing, and displaying
complex information to assist decision-making (Gibson et al. 2017). These systems typically
consist of software components and complex algorithms, designed to support decision-making
with the visualisation of different decision outcomes or scenarios.
   In our interdisciplinary cross-scale approach, we use DSSs to integrate the various data and
models in a way that allows exchange between the researchers and the local decision-makers.

146                                                                    Climatic Change (2019) 156:139–150

The DSSs cover the entire workflow from model development to result analysis and visual-
isation. Since agent-based modelling and simulation systems have a well-proven record to
handle the complexity of coupled human-environmental systems (Le et al. 2012; Lenfers et al.
2018), the MARS (multi-agent research and simulation) framework (Dalski et al. 2017) is used
in the projects described here. MARS is particularly well suited for the simulation of large-
scale scenarios with a high number of individual agents (Hüning et al, 2016).
    We implement decision support via specific case studies at core project research sites. The
first study involves local land-use decision-makers at the Agincourt village, and the second
focuses on livestock farming systems in the Karoo (Fig. 1). The related decision support
systems are, from the outset, designed collaboratively by the MARS developers, the interdis-
ciplinary team of researchers, and the local stakeholders. The active participation of such a
diverse group requires the inclusion of a wide range of knowledge and values (Reed 2008;
Hugé and Mukherjee 2018). We use stakeholder workshops and focus group discussions as the
main way of facilitating exchange between researchers and local decision-makers at the early
stages of planning (Nyumba et al. 2018). The information contained in the land-use planning
tool as well as the format of the tool are designed together with the end users and adapted to
local needs. This is a way to avoid one of the main limitations of many existing DSSs, i.e. their
inability to represent available scientific knowledge in the most appropriate way for the
intended local users (Dicks et al. 2014).
    In addition to generating a practical, hands-on simulation tool for use by land users and/or
decision-makers, this collaborative approach is also an important step towards scaling up
information on the socio-ecological systems and their local impacts on climate into regional
and global level assessments.

4 Conclusion

With multiple pressures of climate change (e.g. Kruger and Shongwe 2004) and land-use change
(e.g. Schoeman et al. 2013), Southern African ecosystems are undergoing substantial changes.
These challenges need to be addressed through an approach that links different disciplines and
measurements at various scales. This paper presents a blueprint for such an approach, while
contributing towards a better understanding of the future of Southern African biodiversity, carbon
sequestration potential, ecosystem services, and livelihoods under the combined impacts of
human land use and climate change. This serves as a basis for developing sustainable and
climate-resilient land-use strategies in cooperation with stakeholders at different levels.

Acknowledgments We gratefully acknowledge the various and crucial contributions of Eva Falge, Wolfgang
Lück, Tetukondjele Iiyambo and Tami Mudau towards the ARS AfricaE project.

Funding information ARS AfricaE and EMSAfrica are part of the SPACES Programme (Science Partnerships
for the Adaptation to Complex Earth System Processes in Southern Africa), funded by the BMBF (German
Federal Ministry of Education and Research) (grant numbers 01LL1303 and 01LL1801) under FONA (Research
for Sustainable Development). We further acknowledge funding from the German Academic Exchange Service
(DAAD) “Capacity Building Südliches Africa”-SPACES ab 12 (57076405). Furthermore, ARS AfricaE was
kindly supported by SANParks through ARS AfricaE EO (project ID SCHCC 1235). SS thanks the DFG
(Deutsche Forschungsgemeinschaft) for funding (grant SCHE1719/2-1). The MODIS NDVI data used in this
study was processed and produced by the NASA/Goddard Space Flight Center’s Global Inventory Modeling and
Mapping Studies (GIMMS) Group through the funding support of the Global Agricultural Monitoring project by
USDA’s Foreign Agricultural Service (FAS).

Climatic Change (2019) 156:139–150                                                                          147

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.

References

Ainsworth EA, Bernacchi CJ, Dohleman FG (2016) Focus on ecophysiology. Plant Physiol 172(2):619–621.
    https://doi.org/10.1104/pp.16.01408
Anadon JD, Sala OE, Turner BL, Bennett EM (2014) Effect of woody-plant encroachment on livestock
    production in North and South America. PNAS 111(35):12948–12953. https://doi.org/10.1073
    /pnas.1320585111
Archibald S (2016) Managing the human component of fire regimes: lessons from Africa. Phil Trans R Soc B
    371(1696):20150346. 1471-2970. https://doi.org/10.1098/rstb.2015.0346
Baldocchi D, Falge E, Gu L et al (2001) FLUXNET: a new tool to study the temporal and spatial variability of
    ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–
    2434. https://doi.org/10.1175/1520-0477(2001)0822.3.CO;2
Baumann P (2017) The datacube manifesto. http://earthserver.eu/tech/datacube-manifesto
Bond WJ, Midgley GF (2012) Carbon dioxide and the uneasy interactions of trees and savannah grasses. Philos
    Trans R Soc B Biol Sci 367:601–612. https://doi.org/10.1098/rstb.2011.0182
Brümmer C, Falk U, Papen H, Szarzynski J, Wassmann R, Brüggemann N (2008) Diurnal, seasonal, and
    interannual variation in carbon dioxide and energy exchange in shrub savanna in Burkina Faso (West
    Africa). J Geophys Res Biogeosci 113:G02030. https://doi.org/10.1029/2007JG000583
Brümmer C, Papen H, Wassmann R, Brüggemann N (2009) Fluxes of CH4 and CO2 from soil and termite
    mounds in south Sudanian savanna of Burkina Faso (West Africa). Glob Biogeochem Cycles 23:GB1001.
    https://doi.org/10.1029/2008GB003237
Brümmer C, Black TA, Jassal RS et al (2012) How climate and vegetation type influence evapotranspiration and
    water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric For Meteorol 153:14–30.
    https://doi.org/10.1016/j.agrformet.2011.04.008
Ciais P, Bombelli A, Williams M et al (2011) The carbon balance of Africa: synthesis of recent research studies.
    Philos Trans R Soc A 369:1–20. https://doi.org/10.1098/rsta.2010.0328
Coetzer KL, Erasmus BFN, Witkowski ETF, Bachoo AK (2010) Land cover change in the Kruger to Canyons
    Biosphere Reserve (1993-2006): a first step towards a conservation plan for the subregion. S Afr J Sci
    106(7–8):1–10 http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-23532010000400009
    &lng=en&nrm=iso.
Cowling RM, Richardson DM, Pierce SM (2004) Vegetation of Southern Africa. Cambridge University Press,
    Cambridge
Cremer F, Urbazaev M, Berger C, Mahecha MD, Schmullius C, Thiel C (2018) An image transform based on
    temporal decomposition. IEEE Geosci Remote Sens Lett 15:537–541. https://doi.org/10.1109
    /LGRS.2018.2791658
Dalski J, Huening C, Clemen T (2017) An Output and 3D visualization concept for the MSAAS system MARS. In:
    Proceedings of the 2017 Spring Simulation Multiconference, Society for Computer Simulation International,
    Virginia Beach, Virginia, USA
Davis-Reddy CL, Vincent K (2017) Climate risk and vulnerability: a handbook for southern Africa, 2nd edn.
    CSIR, Pretoria
DEA (2017). South Africa’s third national communication under the United Nations Framework Convention on Climate
    Change. Tech. rep. Pretoria, South Africa: Department of Environmental Affairs, Republic of South Africa
Dicks LV, Walsh JC, Sutherland WJ (2014) Organising evidence for environmental management decisions: a
    ‘4S’ hierarchy. Trends Ecol Evol 29:607–613. https://doi.org/10.1016/j.tree.2014.09.004
Fisher JT, Witkowski ETF, Erasmus BFN et al (2012) Human-modified landscapes: patterns of fine-scale woody
    vegetation structure in communal savannah rangelands. Environ Conserv 39:72–82. https://doi.org/10.1017
    /S0376892911000592
Gaillard C, Langan L, Pfeiffer M, Kumar D, Martens C, Higgins S, Scheiter S (2018) African shrub distribution
    emerges via a trade-off between height and sapwood conductivity. J Biogeogr 45(12):2815–2826. https://doi.
    org/10.1111/jbi.13447
Giannecchini M, Twine W, Vogel C (2007) Land-cover change and human-environment interactions in a rural
    cultural landscape in South Africa. Geogr J 173:26–42. https://doi.org/10.1111/j.1475-4959.2007.00227.x

148                                                                        Climatic Change (2019) 156:139–150

Gibson FL, Rogers AA, Smith ADM, Roberts A, Possingham H, McCarthy M, Pannell DJ (2017) Factors
    influencing the use of decision support tools in the development and design of conservation policy. Environ
    Sci Pol 70:1–8. https://doi.org/10.1016/j.envsci.2017.01.002
Hickler T, Vohland K, Feehan J et al (2012) Projecting the future distribution of European potential natural
    vegetation zones with a generalized, tree species-based dynamic vegetation model: future changes in
    European vegetation zones. Glob Ecol Biogeogr 21:50–63. https://doi.org/10.1111/j.1466-
    8238.2010.00613.x
Higgins SI, Scheiter S (2012) Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally. Nature
    488:209–212. https://doi.org/10.1038/nature11238
Higgins SI, Shackleton CM, Robinson ER (1999) Changes in woody community structure and composition
    under constrasting landuse systems in a semi-arid savanna, South Africa. J Biogeogr 26:619–627. https://doi.
    org/10.1046/j.1365-2699.1999.t01-1-00317.x
Hugé J, Mukherjee N (2018) The nominal group technique in ecology & conservation: application and
    challenges. Methods Ecol Evol 9(1):33–41. https://doi.org/10.1111/2041-210X.12831
Hüning C, Adebahr M, Clemen T, Dalski J, Lenfers U, Grundmann L (2016) Modeling & simulation as a service
    with the massive multi-agent system MARS. In: Proceedings of the Agent-Directed Simulation Symposium,
    2016. San Diego, CA, USA, p 8
Kahn K, Collinson MA, Gómez-Olive FX, Mokoena O, Twine R, Mee P, Afolabi SA, Clark BD, Kabudula C,
    Khosa A, Khoza S, Shabangu MG, Silaule B, Tibane JB, Wagner RG, Garenne ML, Clark SJ, Tollman SM
    (2012) Profile: Agincourt Health and Socio-demographic Surveillance System. Int J Epidemiol 41(4):988–
    1001. https://doi.org/10.1093/ije/dys115
Kotze I and Rose M (2015) Farming facts and futures: reconnecting South Africa’s food systems to its
    ecosystems. Tech. rep. Cape Town, South Africa: WWF-SA
Kruger AC, Shongwe S (2004) Temperature trends in South Africa: 1960-2003. Int J Climatol 24(15):1929–
    1945. https://doi.org/10.1002/joc.1096
Langevelde FV, Vijver CADMVD, Kumar L et al (2003) Effects of fire and herbivory on the stability of savanna
    ecosystems. Ecology 84:337–350. https://doi.org/10.1890/0012-9658(2003)084[0337:EOFAHO]2.0.CO;2
Le QB, Seidl R, Scholz RW (2012) Feedback loops and types of adaptation in the modelling of land-use
    decisions in an agent-based simulation. Environ Model Softw 27-28:83–96. https://doi.org/10.1016/j.
    envsoft.2011.09.002
Lenfers UA, Weyl J, Clemen T (2018) Firewood collection in South Africa: adaptive behavior in social-
    ecological models. Land 7(3):97. https://doi.org/10.3390/land7030097
Linkov I, Bridges T, Creutzig F et al (2014) Changing the resilience paradigm. Nat Clim Chang 4:407–409.
    https://doi.org/10.1038/nclimate2227
López-Ballesteros A, Beck J, Bombelli A et al (2018) Towards a feasible and representative pan-African research
    infrastructure network for GHG observations. Environ Res Lett 13:085003. https://doi.org/10.1088/1748-
    9326/aad66c
Matsika R, Erasmus BFN, Twine W (2013) Double jeopardy: the dichotomy of fuelwood use in rural South
    Africa. Energy Policy. https://doi.org/10.1016/j.enpol.2012.10.030
Merbold L, Ardö J, Arneth A et al (2009) Precipitation as driver of carbon fluxes in 11 African ecosystems.
    Biogeosciences 6:1027–1041. https://doi.org/10.5194/bg-6-1027-2009
Midgley GF, Bond WJ (2015) Future of African terrestrial biodiversity and ecosystems under anthropogenic
    climate change. Nat Clim Chang 5:–823. https://doi.org/10.1038/nclimate2753
Niang I, Ruppel OC, Abdrabo MA et al (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part
    B: regional aspects. In: Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
    Cambridge University Press, Cambridge and New York, pp 1199–1265
Nyumba T, Wilson K, Derrick CJ, Mukherjee N (2018) The use of focus group discussion methodology: insights
    from two decades of application in conservation. Methods Ecol Evol 9(1):20–32. https://doi.org/10.1111
    /2041-210X.12860
Odipo VO, Nickless A, Berger C, Baade J, Urbazaev M, Walther C, Schmullius C (2016) Assessment of
    aboveground woody biomass dynamics using terrestrial laser scanner and L-band ALOS PALSAR data in
    South African savanna. Forests 7:294. https://doi.org/10.3390/f7120294
Pachzelt A, Forrest M, Rammig A, Higgins S, Hickler T (2015) Potential impact of large ungulate grazers on
    African vegetation, carbon storage and fire regimes. Glob Ecol Biogeogr 24:991–1002. https://doi.
    org/10.1111/geb.12313
Prentice IC, Bondeau A, Cramer W et al (2007) Dynamic global vegetation modeling: quantifying terrestrial
    ecosystem responses to large-scale environmental change. In: Terrestrial ecosystems in a changing world.
    Springer, Berlin, Heidelberg, pp 175–192

Climatic Change (2019) 156:139–150                                                                            149

Rabin SS, Melton JR, Lasslop G et al (2017) The Fire Modeling Intercomparison Project (FireMIP), phase 1:
     experimental and analytical protocols. Geosci Model Dev 20:1175–1197. https://doi.org/10.5194/gmd-10-
     1175-2017
Reed MS (2008) Stakeholder participation for environmental management: a literature review. Biol Conserv
     141(10):2417–2431. https://doi.org/10.1016/j.biocon.2008.07.014
Scheiter S, Higgins SI (2009) Impacts of climate change on the vegetation of Africa: an adaptive dynamic
     vegetation modelling approach. Glob Chang Biol 15(9):2224–2246. https://doi.org/10.1111/j.1365-
     2486.2008.01838.x
Scheiter S, Higgins SI (2012) How many elephants can you fit into a conservation area. Conserv Lett 5(3):176–
     185. https://doi.org/10.1111/j.1755-263X.2012.00225.x
Scheiter S, Schulte J, Pfeiffer M, Martens C, Erasmus BFN, Twine W (2019) How does climate change influence
     the economic value of ecosystem services in savanna rangelands? Ecol Econ 157:342–356. https://doi.
     org/10.1016/j.ecolecon.2018.11.015
Schoeman F, Newby TS, Thompson MW, den Berg EV (2013) South African national land-cover change map.
     South Afr J Geomat 2:94–105
Seiler C, Hutjes RWA, Kruijt B, Quispe J, Añez S, Arora VK, Melton JR, Hickler T, Kabat P (2014) Modeling
     forest dynamics along climate gradients in Bolivia. J Geophys Res Biogeosci 119:758–775. https://doi.
     org/10.1002/2013JG002509
Skowno AL, Thompson MW, Hiestermann J, Ripley B, West AG, Bond WJ (2017) Woodland expansion in
     South African grassy biomes based on satellite observations (1990-2013): general patterns and potential
     drivers. Glob Chang Biol 23(6):2358–2369. https://doi.org/10.1111/gcb.13529
Smit IPJ, Prins HHT (2015) Predicting the effects of woody encroachment on mammal communities, grazing
     biomass and fire frequency in African savannas. PLoS One 10:e0137857. https://doi.org/10.1371/journal.
     pone.0137857
Smith B, Prentice IC, Sykes MT (2001) Representation of vegetation dynamics in the modelling of terrestrial
     ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeogr 10:
     621–637. https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x
Smith B, Warlind D, Arneth A, Hickler T, Leadley P, Siltberg J, Zaehle S (2014) Implications of incorporating N
     cycling and N limitations on primary production in an individual-based dynamic vegetation model.
     Biogeosciences 11:2027–2054. https://doi.org/10.5194/bg-11-2027-2014
Stanton RA, Boone WW, Soto-Shoender J, Fletcher RJ Jr, Blaum N, McCleery RA (2018) Shrub encroachment
     and vertebrate diversity: a global meta-analysis. Glob Ecol Biogeogr 27:368–379. https://doi.org/10.1111
     /geb.12675
Stevens N, Bond WJ, Hoffman T, Midgley G (2015) Change is in the air - ecological trends and their drivers in
     South Africa. South African Environmental Observation Network (SAEON)
Stevens N, Erasmus BFN, Archibald S, Bond WJ (2016) Woody encroachment over 70 years in South African
     savannahs: overgrazing, global change or extinction aftershock? Philos Trans R Soc B 371:20150437.
     https://doi.org/10.1098/rstb.2015.0437
Tadross M, Engelbrecht F, Jack C, Wolski P, Davis-Reddy CL (2017) Projected climate change futures for
     Southern Africa. In: Climate risk and vulnerability: a handbook for Southern Africa 2nd edn. CSIR, Pretoria
Urban M, Berger C, Mudau A, Heckel K, Truckenbrodt J, Odipo V, Smit I, Schmullius C (2018) Surface
     moisture and vegetation cover analysis for drought monitoring in the southern Kruger National Park using
     Sentinel-1, Sentinel-2, and Landsat-8. Remote Sens 10(9):1482. https://doi.org/10.3390/rs10091482
Urbazaev M, Thiel C, Mathieu R et al (2015) Assessment of the mapping of fractional woody cover in southern
     African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images. Remote Sens
     Environ 166:138–153. https://doi.org/10.1016/j.rse.2015.06.013
Valentini R, Arneth A, Bombelli A et al (2014) A full greenhouse gases budget of Africa: synthesis, uncertainties,
     and vulnerabilities. Biogeosciences 11:381–407. https://doi.org/10.5194/bg-11-381-2014
Veenendaal EM, Kolle O, Lloyd J (2004) Seasonal variation in energy fluxes and carbon dioxide exchange for a
     broad-leaved semi-arid savanna (mopane woodland) in Southern Africa. Glob Chang Biol 10:318–328.
     https://doi.org/10.1111/j.1365-2486.2003.00699.x
Ziervogel G, New M, van Garderen EA, Midgley G, Taylor A, Hamann R, Stuart-Hill S, Myers J, Warburton M
     (2014) Climate change impacts and adaptation in South Africa. Wiley Interdiscip Rev Clim Chang 5:605–
     620. https://doi.org/10.1002/wcc.295

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.

150                                                                       Climatic Change (2019) 156:139–150

Affiliations

Christian Berger 1 & Mari Bieri 2 & Karen Bradshaw 3 & Christian Brümmer 2 & Thomas
Clemen 4 & Thomas Hickler 5,6 & Werner Leo Kutsch 7 & Ulfia A. Lenfers 4 & Carola
Martens 5,6 & Guy F. Midgley 8 & Kanisios Mukwashi 2 & Victor Odipo 1 & Simon Scheiter 5 &
Christiane Schmullius 1 & Jussi Baade 9 & Justin C. O. du Toit 10 & Robert J. Scholes 11 & Izak
P. J. Smit 12,13 & Nicola Stevens 8 & Wayne Twine 14
1
      Department for Earth Observation, Friedrich Schiller University Jena, Löbdergraben 32, 07743 Jena,
      Germany
2
      Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany
3
      Department of Computer Science, Rhodes University, Grahamstown 6140, South Africa
4
      Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7,
      20099 Hamburg, Germany
5
      Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325 Frankfurt
      am Main, Germany
6
      Institute of Physical Geography, Goethe-University Frankfurt am Main, Altenhöferallee 1, 60438 Frankfurt
      am Main, Germany
7
      Integrated Carbon Observation System, European Research Infrastructure Consortium (ICOS ERIC), Head
      Office, Erik-Palménin-Aukio 1, 00560 Helsinki, Finland
8
      Department of Botany and Zoology, Stellenbosch University, Stellenbosch 7600, South Africa
9
      Department of Geography, Friedrich Schiller University Jena, Löbdergraben 32, 07743 Jena, Germany
10
      Grootfontein Agriculture Development Institute, Middelburg, Eastern Cape, South Africa
11
      Global Change Institute, University of the Witwatersrand, Johannesburg 2050, South Africa
12
      Scientific Services, South African National Parks, Private Bag X402, Skukuza 1350, South Africa
13
      Centre for African Ecology, School of Animal, Plant and Environmental Sciences, University of the
      Witwatersrand, Johannesburg 2050, South Africa
14
      School of Animal, Plant and Environmental Sciences, University of the Witwatersrand,
      Johannesburg 2050, South Africa